{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST 2.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "nZm3k_JwAxFK",
        "colab_type": "code",
        "outputId": "7550ff90-0b1e-4dbd-e6a9-bc87ff87e1b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "cell_type": "code",
      "source": [
        "!pip3 install torch torchvision"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.0.0)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (0.2.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.14.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.11.0)\n",
            "Collecting pillow>=4.1.1 (from torchvision)\n",
            "  Using cached https://files.pythonhosted.org/packages/85/5e/e91792f198bbc5a0d7d3055ad552bc4062942d27eaf75c3e2783cf64eae5/Pillow-5.4.1-cp36-cp36m-manylinux1_x86_64.whl\n",
            "Installing collected packages: pillow\n",
            "  Found existing installation: Pillow 4.0.0\n",
            "    Uninstalling Pillow-4.0.0:\n",
            "      Successfully uninstalled Pillow-4.0.0\n",
            "Successfully installed pillow-5.4.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5oX6ISEIaA5k",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "PKcBS6xfBK1Z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch.nn.functional as F\n",
        "from torch import nn\n",
        "from torchvision import datasets, transforms"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oeOT_fh3uoGj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hC4ZVkTRB79P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "transform = transforms.Compose([transforms.Resize((28,28)),\n",
        "                               transforms.ToTensor(),\n",
        "                               transforms.Normalize((0.5,), (0.5,))\n",
        "                               ])\n",
        "training_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
        "validation_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
        "\n",
        "training_loader = torch.utils.data.DataLoader(training_dataset, batch_size=100, shuffle=True)\n",
        "validation_loader = torch.utils.data.DataLoader(validation_dataset, batch_size = 100, shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jCqX-QWODId3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def im_convert(tensor):\n",
        "  image = tensor.cpu().clone().detach().numpy()\n",
        "  image = image.transpose(1, 2, 0)\n",
        "  image = image * np.array((0.5, 0.5, 0.5)) + np.array((0.5, 0.5, 0.5))\n",
        "  image = image.clip(0, 1)\n",
        "  return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8bUYaxqxGFET",
        "colab_type": "code",
        "outputId": "894ae82e-5128-405c-e2bc-ce763febb972",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "cell_type": "code",
      "source": [
        "dataiter = iter(training_loader)\n",
        "images, labels = dataiter.next()\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "\n",
        "for idx in np.arange(20):\n",
        "  ax = fig.add_subplot(2, 10, idx+1, xticks=[], yticks=[])\n",
        "  plt.imshow(im_convert(images[idx]))\n",
        "  ax.set_title([labels[idx].item()])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fdeb4de46d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ELDFsBKeHqJy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class LeNet(nn.Module):\n",
        "    def __init__(self):\n",
        "      super().__init__()\n",
        "      self.conv1 = nn.Conv2d(1, 20, 5, 1)\n",
        "      self.conv2 = nn.Conv2d(20, 50, 5, 1)\n",
        "      self.fc1 = nn.Linear(4*4*50, 500)\n",
        "      self.dropout1 = nn.Dropout(0.5)\n",
        "      self.fc2 = nn.Linear(500, 10)\n",
        "    def forward(self, x):\n",
        "      x = F.relu(self.conv1(x))\n",
        "      x = F.max_pool2d(x, 2, 2)\n",
        "      x = F.relu(self.conv2(x))\n",
        "      x = F.max_pool2d(x, 2, 2)\n",
        "      x = x.view(-1, 4*4*50)\n",
        "      x = F.relu(self.fc1(x))\n",
        "      x = self.dropout1(x)\n",
        "      x = self.fc2(x)\n",
        "      return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cOa--7ggN8Nu",
        "colab_type": "code",
        "outputId": "fa47fe06-9454-4456-dad4-c43a22c61826",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "model = LeNet().to(device)\n",
        "model"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LeNet(\n",
              "  (conv1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))\n",
              "  (conv2): Conv2d(20, 50, kernel_size=(5, 5), stride=(1, 1))\n",
              "  (fc1): Linear(in_features=800, out_features=500, bias=True)\n",
              "  (dropout1): Dropout(p=0.5)\n",
              "  (fc2): Linear(in_features=500, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "metadata": {
        "id": "w6wxPBg_Od3t",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = 0.0001)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zsd3HPylP9UE",
        "colab_type": "code",
        "outputId": "0ef136c2-35b1-4afd-ad48-abbf94a9a1fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        }
      },
      "cell_type": "code",
      "source": [
        "epochs = 15\n",
        "running_loss_history = []\n",
        "running_corrects_history = []\n",
        "val_running_loss_history = []\n",
        "val_running_corrects_history = []\n",
        "\n",
        "for e in range(epochs):\n",
        "  \n",
        "  running_loss = 0.0\n",
        "  running_corrects = 0.0\n",
        "  val_running_loss = 0.0\n",
        "  val_running_corrects = 0.0\n",
        "  \n",
        "  for inputs, labels in training_loader:\n",
        "    inputs = inputs.to(device)\n",
        "    labels = labels.to(device)\n",
        "    outputs = model(inputs)\n",
        "    loss = criterion(outputs, labels)\n",
        "    \n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    \n",
        "    _, preds = torch.max(outputs, 1)\n",
        "    running_loss += loss.item()\n",
        "    running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "  else:\n",
        "    with torch.no_grad():\n",
        "      for val_inputs, val_labels in validation_loader:\n",
        "        val_inputs = val_inputs.to(device)\n",
        "        val_labels = val_labels.to(device)\n",
        "        val_outputs = model(val_inputs)\n",
        "        val_loss = criterion(val_outputs, val_labels)\n",
        "        \n",
        "        _, val_preds = torch.max(val_outputs, 1)\n",
        "        val_running_loss += val_loss.item()\n",
        "        val_running_corrects += torch.sum(val_preds == val_labels.data)\n",
        "      \n",
        "    epoch_loss = running_loss/len(training_loader)\n",
        "    epoch_acc = running_corrects.float()/ len(training_loader)\n",
        "    running_loss_history.append(epoch_loss)\n",
        "    running_corrects_history.append(epoch_acc)\n",
        "    \n",
        "    val_epoch_loss = val_running_loss/len(validation_loader)\n",
        "    val_epoch_acc = val_running_corrects.float()/ len(validation_loader)\n",
        "    val_running_loss_history.append(val_epoch_loss)\n",
        "    val_running_corrects_history.append(val_epoch_acc)\n",
        "    print('epoch :', (e+1))\n",
        "    print('training loss: {:.4f}, acc {:.4f} '.format(epoch_loss, epoch_acc.item()))\n",
        "    print('validation loss: {:.4f}, validation acc {:.4f} '.format(val_epoch_loss, val_epoch_acc.item()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch : 1\n",
            "training loss: 0.5399, acc 85.4417 \n",
            "validation loss: 0.1702, validation acc 94.9800 \n",
            "epoch : 2\n",
            "training loss: 0.1434, acc 95.7533 \n",
            "validation loss: 0.1013, validation acc 96.9000 \n",
            "epoch : 3\n",
            "training loss: 0.0959, acc 97.1350 \n",
            "validation loss: 0.0740, validation acc 97.9100 \n",
            "epoch : 4\n",
            "training loss: 0.0745, acc 97.7900 \n",
            "validation loss: 0.0602, validation acc 98.1400 \n",
            "epoch : 5\n",
            "training loss: 0.0626, acc 98.1000 \n",
            "validation loss: 0.0511, validation acc 98.3500 \n",
            "epoch : 6\n",
            "training loss: 0.0541, acc 98.3717 \n",
            "validation loss: 0.0517, validation acc 98.3400 \n",
            "epoch : 7\n",
            "training loss: 0.0484, acc 98.5233 \n",
            "validation loss: 0.0408, validation acc 98.6900 \n",
            "epoch : 8\n",
            "training loss: 0.0434, acc 98.6767 \n",
            "validation loss: 0.0385, validation acc 98.8100 \n",
            "epoch : 9\n",
            "training loss: 0.0380, acc 98.8500 \n",
            "validation loss: 0.0374, validation acc 98.7700 \n",
            "epoch : 10\n",
            "training loss: 0.0350, acc 98.9283 \n",
            "validation loss: 0.0347, validation acc 98.9700 \n",
            "epoch : 11\n",
            "training loss: 0.0323, acc 99.0400 \n",
            "validation loss: 0.0330, validation acc 98.9000 \n",
            "epoch : 12\n",
            "training loss: 0.0302, acc 99.0617 \n",
            "validation loss: 0.0324, validation acc 98.9300 \n",
            "epoch : 13\n",
            "training loss: 0.0271, acc 99.1700 \n",
            "validation loss: 0.0317, validation acc 99.0200 \n",
            "epoch : 14\n",
            "training loss: 0.0246, acc 99.2650 \n",
            "validation loss: 0.0293, validation acc 98.9300 \n",
            "epoch : 15\n",
            "training loss: 0.0231, acc 99.2717 \n",
            "validation loss: 0.0300, validation acc 99.0300 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qq4-LouHVQwp",
        "colab_type": "code",
        "outputId": "f002f868-32df-462c-c6e4-608c2e2a9d92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(running_loss_history, label='training loss')\n",
        "plt.plot(val_running_loss_history, label='validation loss')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdeb3bf07b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RIiIyLFk6hCHdGt7b3Ek8kcx3UURERHqxfgiXeEilYG9TZ76LIiIi0ov1Q1hrSIuIyDBV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\nkTxRCIuIiOSJQlhERCRPFMIiIiJ58v8Bw3KWRvVAmjkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fdeb3c2ecf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "R1A-fOideMnx",
        "colab_type": "code",
        "outputId": "dd266a0e-ed95-4eef-ba06-74bfe2590ffe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(running_corrects_history, label='training accuracy')\n",
        "plt.plot(val_running_corrects_history, label='validation accuracy')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdeb3ba0cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F/cYyILtvzM9VpGFGIiISA50ufONJs1uJiEgsKHwPo36YkdPe6a7Oi4hIB6bw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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fdeb3b88a20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vEsatUk1EmTp",
        "colab_type": "code",
        "outputId": "04acf815-0987-4a7c-d235-5a85c82145c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        }
      },
      "cell_type": "code",
      "source": [
        "!pip3 install pillow==4.0.0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pillow==4.0.0\n",
            "  Using cached https://files.pythonhosted.org/packages/37/e8/b3fbf87b0188d22246678f8cd61e23e31caa1769ebc06f1664e2e5fe8a17/Pillow-4.0.0-cp36-cp36m-manylinux1_x86_64.whl\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow==4.0.0) (0.46)\n",
            "\u001b[31mtorchvision 0.2.1 has requirement pillow>=4.1.1, but you'll have pillow 4.0.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: pillow\n",
            "  Found existing installation: Pillow 5.4.1\n",
            "    Uninstalling Pillow-5.4.1:\n",
            "      Successfully uninstalled Pillow-5.4.1\n",
            "Successfully installed pillow-4.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Bdd5aONjGO5_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import PIL.ImageOps"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gUPLBiV0gDd8",
        "colab_type": "code",
        "outputId": "7520f7db-82c0-4e41-bf80-b0018eb59af5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from PIL import Image\n",
        "\n",
        "url = 'https://images.homedepot-static.com/productImages/007164ea-d47e-4f66-8d8c-fd9f621984a2/svn/architectural-mailboxes-house-letters-numbers-3585b-5-64_1000.jpg'\n",
        "response = requests.get(url, stream = True)\n",
        "img = Image.open(response.raw)\n",
        "plt.imshow(img)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fdeb39bd748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vSVEmkjEJ8UMPPYRZs2Zh991393y/nK+okyv1AyPUn//85+N+7HXr1o37MRWl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\nDDqZYAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fdeb2268400>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "v24yJujOERx8",
        "colab_type": "code",
        "outputId": "1f960a7d-2725-42d1-8836-206f93c2671b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "cell_type": "code",
      "source": [
        "img = PIL.ImageOps.invert(img)\n",
        "img = img.convert('1')\n",
        "img = transform(img) \n",
        "plt.imshow(im_convert(img))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fdeb3962fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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woQ4fPqzJyUmVlpbq9OnTCoVCTi9zVv49U319vQYGBlRcXCxJ2rVrl9avX+/sImepublZ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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fdeb2406748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Gd_XSROHFzsd",
        "colab_type": "code",
        "outputId": "7e419ffa-30cb-42be-c2f7-cd77333319b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "images = img.to(device)\n",
        "image = images[0].unsqueeze(0).unsqueeze(0)\n",
        "output = model(image)\n",
        "_, pred = torch.max(output, 1)\n",
        "print(pred.item())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UE7haDIMG2cE",
        "colab_type": "code",
        "outputId": "97a24932-316a-4957-9b56-2423bdc787cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "cell_type": "code",
      "source": [
        "dataiter = iter(validation_loader)\n",
        "images, labels = dataiter.next()\n",
        "images = images.to(device)\n",
        "labels = labels.to(device)\n",
        "output = model(images)\n",
        "_, preds = torch.max(output, 1)\n",
        "\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "\n",
        "for idx in np.arange(20):\n",
        "  ax = fig.add_subplot(2, 10, idx+1, xticks=[], yticks=[])\n",
        "  plt.imshow(im_convert(images[idx]))\n",
        "  ax.set_title(\"{} ({})\".format(str(preds[idx].item()), str(labels[idx].item())), color=(\"green\" if preds[idx]==labels[idx] else \"red\"))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fdeb3752dd8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "nzLUhaxBIHwB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}
